def constructTree(tree):
## 1. ini tree node
index2node = {}
for i in tree:
node = BU_RvNN.Node_tweet(idx=i)
index2node[i] = node
## 2. construct tree
for j in tree:
indexC = j
indexP = tree[j]['parent']
nodeC = index2node[indexC]
wordFreq, wordIndex = str2matrix(tree[j]['vec'], tree[j]['maxL'])
nodeC.index = wordIndex
nodeC.word = wordFreq
## not root node ##
if not indexP == 'None':
nodeP = index2node[int(indexP)]
nodeC.parent = nodeP
nodeP.children.append(nodeC)
## root node ##
else:
root = nodeC
## 3. convert tree to DNN input
degree = tree[j]['max_degree']
x_word, x_index, tree = BU_RvNN.gen_nn_inputs(root, max_degree=degree, only_leaves_have_vals=False)
return x_word, x_index, tree
def construct_tree(tree):
# tree: {index1:{'parent':, 'maxL':, 'vec':}
# 1. ini tree node
index2node = {}
for i in tree:
node = BU_RvNN.NodeTweet(idx=i)
index2node[i] = node
# 2. construct tree
_j = 0
root = None
for _j in tree:
index_c = _j
index_p = tree[_j]['parent']
node_c = index2node[index_c]
word_freq, word_index = str2matrix(tree[_j]['vec'], tree[_j]['maxL'])
node_c.index = word_index
node_c.word = word_freq
# not root node
if not index_p == 'None':
node_p = index2node[int(index_p)]
node_c.parent = node_p
node_p.children.append(node_c)
# root node
else:
root = node_c
# 3. convert tree to DNN input
degree = tree[_j]['max_degree']
x_word, x_index, tree = BU_RvNN.gen_nn_inputs(root, max_degree=degree, only_leaves_have_vals=False)
return x_word, x_index, tree